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A global model for estimating the block time of commercial passenger aircraft

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  • Coy, Steven

Abstract

This paper uses a two-stage statistical model to estimate the block time of commercial passenger aircraft. The model considers many of the factors contributing to airport congestion and provides a basis for future development of multivariate statistical models of the flight operation process. The model is tested using 2 million US domestic flights by six airlines in 2004. Model analysis provided insight regarding the relative impact of weather conditions and airport utilization on block time. In particular, population, arrival time, airport utilization, ice, and the interaction of poor weather conditions and traffic were found to be significant predictors of block time.

Suggested Citation

  • Coy, Steven, 2006. "A global model for estimating the block time of commercial passenger aircraft," Journal of Air Transport Management, Elsevier, vol. 12(6), pages 300-305.
    • Handle: RePEc:eee:jaitra:v:12:y:2006:i:6:p:300-305
    DOI: 10.1016/j.jairtraman.2006.07.005
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    References listed on IDEAS

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    Cited by:

    1. Linlin Chen & Shuihua Han & Chaokan Du & Zongwei Luo, 2022. "A real-time integrated optimization of the aircraft holding time and rerouting under risk area," Annals of Operations Research, Springer, vol. 310(1), pages 7-26, March.
    2. Abdelghany, Ahmed & Abdelghany, Khaled & Guzhva, Vitaly S., 2024. "Schedule-level optimization of flight block times for improved airline schedule planning: A data-driven approach," Journal of Air Transport Management, Elsevier, vol. 115(C).
    3. Wang, Chunzheng & Hu, Minghua & Yang, Lei & Zhao, Zheng, 2022. "Improving the spatial-temporal generalization of flight block time prediction: A development of stacking models," Journal of Air Transport Management, Elsevier, vol. 103(C).
    4. Kang, Lei & Hansen, Mark, 2017. "Behavioral analysis of airline scheduled block time adjustment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 56-68.
    5. Abdelghany, Ahmed & Guzhva, Vitaly S. & Abdelghany, Khaled, 2023. "The limitation of machine-learning based models in predicting airline flight block time," Journal of Air Transport Management, Elsevier, vol. 107(C).
    6. Calzada, Joan & Fageda, Xavier, 2023. "Airport dominance, route network design and flight delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    7. Liu, Ke & Zheng, Zhe & Zou, Bo & Hansen, Mark, 2023. "Airborne flight time: A comparative analysis between the U.S. and China," Journal of Air Transport Management, Elsevier, vol. 107(C).
    8. Hao, Lu & Hansen, Mark, 2014. "Block time reliability and scheduled block time setting," Transportation Research Part B: Methodological, Elsevier, vol. 69(C), pages 98-111.

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